Transdiagnostic, internet-delivered cognitive behavior therapy for depression and anxiety: Exploring impact on health anxiety
Bibliographic record
Abstract
Health anxiety is associated with significant personal distress and economic cost; as such, widely available and effective treatment options are crucial. Several studies suggest that Internet-delivered cognitive behavior therapy (ICBT) programs that specifically target health anxiety are efficacious for this condition. However, no known studies have examined the impact of transdiagnostic ICBT, which emphasizes the acquisition of broad coping skills applicable to a variety of mental health concerns, on symptoms of health anxiety. The current study sought to explore changes in health anxiety symptoms by utilizing data available from a previously published study of 8-week transdiagnostic ICBT. Specifically, changes in symptoms of health anxiety in response to a transdiagnostic ICBT program that targeted broad symptoms of depression and anxiety, were examined in a subsample of individuals who endorsed elevated symptom scores on the Short Health Anxiety Inventory at pre-treatment ( n = 72). Following treatment, large reductions in health anxiety symptoms (Cohen's d = 0.91; 20% improvement), depression (Cohen's d = 0.85; reduction = 38%), generalized anxiety (Cohen's d = 1.21; reduction = 42%), and disability (Cohen's d = 0.90; reduction = 35%) were reported. Furthermore, results showed that transdiagnostic ICBT was rated as acceptable to people with high health anxiety symptoms. Despite elevated pre-treatment health anxiety scores, email correspondence between clients and their therapist revealed very few mentions of health anxiety. These findings provide preliminary evidence for transdiagnostic ICBT for symptoms of health anxiety and suggest further research is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".